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README.md
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# Generative Embedding Benchmark
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<div align="center">
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<a href="https://huggingface.co/datasets/LimitedMouse/Generative-Embedding-Benchmark"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue" alt="Hugging Face Dataset"></a>
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<a href="https://github.com/LimitedMouse/Generative-Embedding-Benchmark"><img src="https://img.shields.io/badge/GitHub-Code-lightgrey" alt="GitHub Code"></a>
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</div>
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This repository publishes the fixed membership and provenance metadata for the
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Generative Embedding Benchmark (GEB). GEB contains 1,800 development questions
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and 900 held-out test questions
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GEB evaluates how much answer-relevant visual information a dense embedding
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makes accessible to a generative decoder.
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for installation
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## Files
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# Generative Embedding Benchmark
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This repository publishes the fixed membership and provenance metadata for the
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Generative Embedding Benchmark (GEB). GEB contains 1,800 development questions
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and 900 held-out test questions spanning natural images, scene text, and visual
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documents.
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GEB evaluates how much answer-relevant visual information a dense embedding
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makes accessible to a generative decoder. The
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[GitHub repository](https://github.com/LimitedMouse/Generative-Embedding-Benchmark)
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is the main entry point for installation, training, and evaluation. Official
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Qwen3-VL-Embedding-2B/8B decoder weights are provided in the
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[checkpoint repository](https://huggingface.co/LimitedMouse/Generative-Embedding-Benchmark-Checkpoints).
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This dataset repository does not redistribute source images, questions,
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answers, or decoder training data. GEB task definitions load the eight original
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public evaluation datasets through `lmms-eval` and filter them with `dev.json`
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or `test.json`. The source datasets remain subject to their own licenses and
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access requirements.
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## Files
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